Hey! I want to ensure data safety and privacy in an enterprise grade dataset used in a Crew Ai model. I researched about Isolated environments and RAG Pipelines but am unsure about how to implement them or use them.
Let’s say you’re working with sensitive customer support data. You could set up an isolated environment on a secure cloud instance, with a RAG pipeline that retrieves relevant support documents from a database. The model would then generate responses based on this data, all within the isolated environment. Access to this environment and the data within it would be tightly controlled and monitored.
Next Steps
- Set up the isolated environment using a cloud provider like AWS, Azure, or GCP.
- Index your dataset using a vector database.
- Build and deploy the RAG pipeline within the isolated environment.
- Implement encryption, access control, and monitoring throughout the process.
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Umm, since I’ll be using a pre-trained model like ChatGPT or Anthropic, wouldn’t they raise an issue in privacy as they store data for training??
That reply in this thread appears to be generated by a chat tool. The “Next Steps” in bold text followed by a numbered list is common for chat-generated text.
I would not trust it very much.
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No data privacy the moment you are using ChatGPT 
Only way would be creating your own database and creating your own AI tools as you involve any third party, your data privacy concern would need a holistic agreement from all the parties involved which would always be concern.
But one way would be when you are using pre-trained model also, you make sure you provided your data with conditional encryption, for which no other parties other than you have access.
How you would create this conditional encryption would be your creative idea to look forward 
All the best
Regards
DP
It is chat generated, though the general idea of RAG is what I was thinking of as well
Data encryption would be a big hassle as the task requires text extraction from PDF’s/Doc’s. I will look more into using a vector database and regulating the data sent through a cloud service. However, from what I researched fine-tuning and implementing RAG may resolve my concerns. Alas, if nothing works, I’ll unwillingly encrypt the data TT__TT.
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in case you are interested there is a short course, it is not directly pertaining to your query but has some useful explanation pertaining to using gemini with external APIs.
So in case you want to test the course, please refer the link below (if you want to test the short course, you would be required to fill form) https://forms.gle/HhQWMzAZpfQYvudy5
Regards
DP